Social network applications commonly refer to applications that facilitate interaction of individuals through various websites or other Internet-based distribution of content. In most social network applications a user can create an account and provide various types of content specific to the individual, such as pictures of the individual, their friends, their family, personal information in text form, favorite music or videos, etc. The content is then made available to other users of the social network application. For example, one or more web pages may be defined for each user of the social network application that can be viewed by other users of the social network application. Also, social network applications typically allow a user to define a set of “friends,” “contacts” or “members” with whom the respective user wishes to communicate repeatedly. In general, users of a social network application may post comments or other content to portions of each other's web pages.
Typically, the user's content is updated periodically to reflect the most recent or most significant occurrences in the user's life. This process involves selecting new content, editing the presentation of the existing content within one or more web pages to include the selected new content, and uploading any changes to a social network server. Of course, often it is not convenient to update content on a social network site while an event or social function is still occurring. As a result, the user's “friends” are unable to view content relating to the event or social function until some time after the event or social function has ended. The inability to interact with the user in real time, via the social networking site, may increase the feeling of alienation that the user's “friends” experience due to being unable to attend the event or social function in person. Furthermore, depending on the user's dedication to maintaining a current profile, significant time may elapse between the end of an event or social function and updating of the profile. Unfortunately, it is often the case that the “real-time value” of the captured image is lost. As a result, the user's “friends” do not realize that a particular person has entered a party or a bar, or that a beautiful sunset is occurring, etc., until after it is too late to act on that information.
It is also a common occurrence for users of social network applications to neglect to capture images during events or social functions, or to capture images that are of poor quality, etc. The user may discover after the fact that they do not have suitable images of certain people that they would like to feature in the updated content relating to a particular event or social function. At the same time, the user may inadvertently have captured images of individuals who object to being depicted on social network sites. For these reasons, even if the user is dedicated to maintaining a current profile, the result tends to be less that optimal.
Of course, images are captured for a variety of reasons other than for populating social network web pages. For instance, images are typically captured for reasons associated with security and/or monitoring. By way of a specific and non-limiting example, a parent may wish to monitor the movements of a young child within an enclosed area that is equipped with a camera system. When several children are present within the enclosed area, the captured images are likely to include images of at least some of the other children, and as a result the young child may be hidden in some of the images. Under such conditions, the parent must closely examine each image to pick out the young child that is being monitored. Another example relates to the tracking of objects in storage areas or transfer stations, etc.
Complex matching and object identification methods are known for tracking the movement of objects, such as is described in United States Patent Application Publication 2009/0245573 A1, the entire contents of which are incorporated herein by reference. Image data captured in multiple fields of view are analyzed to detect objects, and a signature of features is determined for the objects that are detected in each field of view. Via a learning process, the system compares the signatures for each of the objects to determine if the objects are multiple occurrences of the same object. Unfortunately, the system must be trained in a semi-manual fashion, and the training must be repeated for every classification of object that is to be analyzed.
It would be advantageous to provide a method and system that overcomes at least some of the above-mentioned limitations.